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COVID-19 recommender system based on an annotated multilingual corpus.

Márcia Barros1,2, Pedro Ruas1, Diana Sousa1

  • 1Large-Scale Informatics Systems Laboratory, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal.

Genomics & Informatics
|October 12, 2021
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Summary
This summary is machine-generated.

Researchers created a multilingual dataset to improve text mining for Coronavirus disease 2019 (COVID-19) research. This resource aids in tracking vital COVID-19 information across languages, enhancing scientific discovery.

Keywords:
COVID-19entity extractionrecommendationrelation extractiontext mining

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Area of Science:

  • Natural Language Processing
  • Biomedical Informatics
  • Computational Linguistics

Background:

  • The rapid increase in Coronavirus disease 2019 (COVID-19) research necessitates automated methods for information tracking.
  • Existing text mining approaches are limited by a scarcity of curated, multilingual corpora for COVID-19.

Purpose of the Study:

  • To address the lack of multilingual resources for COVID-19 text mining.
  • To develop and share a curated, multilingual parallel corpus and recommendation dataset for COVID-19 literature.

Main Methods:

  • Annotation of a multilingual parallel corpus focusing on English, Portuguese, and Spanish.
  • Generation of a recommendation dataset identifying entities and relations within COVID-19 literature.

Main Results:

  • Creation of an annotated multilingual parallel corpus for COVID-19 research.
  • Development of a recommendation dataset (EN-PT and EN-ES) for entity and relation extraction.
  • Provision of a valuable resource to the research community.

Conclusions:

  • The developed multilingual corpus and recommendation dataset will significantly enhance text mining research in COVID-19.
  • This resource facilitates improved information retrieval and knowledge discovery from the growing body of COVID-19 literature.
  • The project underscores the importance of collaborative efforts like the Biomedical Linked Annotation Hackathon (BLAH) in advancing scientific research.